Merging Jacobi and Gauss-Seidel methods for solving Markov chains on computer clusters

Abstract

The authors consider the use of the parallel iterative methods for solving large sparse linear equation systems resulting from Markov chains-on a computer cluster. A combination of Jacobi and Gauss-Seidel iterative methods is examined in a parallel version. Some results of experiments for sparse systems with over 3 times 107 equations and about 2 times 108 nonzeros which we obtained from a Markovian model of a congestion control mechanism are reported.